AWSの機械学習基盤を使ってみよう

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AWSͷػցֶशج൫Λ࢖ͬͯΈΑ͏ ౔ ߹ಉษڧձ in େ౎ձԬࢁ -2017 Winter- ాத޹໌

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"CPVUNF

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wΫϥεϝιουגࣜձࣾ wϞόΠϧΞϓϦαʔϏε෦ wJ04ΞϓϦΤϯδχΞ wαʔόʔαΠυΞϓϦΤϯδχΞ wαʔόʔϨε։ൃ෦ wΞϓϦέʔγϣϯΤϯδχΞ ాத޹໌ @kongmingtrap

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ాத޹໌ @kongmingtrap Ԭࢁग़਎

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ాத޹໌ @kongmingtrap ෱Ԭࡏॅ

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IUUQTDMBTTNFUIPEKQOFXTOFXP⒏DFGVLVPLB

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IUUQTDMBTTNFUIPEKQOFXTOFXP⒏DFGVLVPLB ෱ԬҠॅʂʂʂ

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ؓ࿩ٳ୊

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ࠓ೥ͷػցֶश ϋΠϥΠτ

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w5FOTPS'MPX-JUF wߴ଎͔ͭܰྔͳΞϓϦ޲͚ػցֶशϑϨʔϜϫʔΫ w5FOTPS'MPX3FTFBSDI$PVME wτϨʔχϯά͓Αͼਪ࿦ͷ྆ํΛߴ଎Խ͢Δ·ͬͨ ͘৽͍͠(PPHMFͷΫϥ΢υ516 (PPHMF*0

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w$PSF.- wֶशϞσϧ౳ΛJ04NBD04্Ͱར༻͢Δࡍʹɺ ։ൃऀ͕ઐ໳తͳ஌ࣝΛඞཁͱͤͣʹѻ͑ΔΑ͏ʹ ิॿ͢ΔϑϨʔϜϫʔΫ wDPSFNMUPPMT wػցֶशϑϨʔϜϫʔΫͰ࡞੒ֶͨ͠शϞσϧΛ $PSF.-Ͱར༻Ͱ͖ΔΑ͏ʹม׵ 88%$

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w"84%FFQ-FOT SF*OWFOU

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w"84%FFQ-FOT wσΟʔϓϥʔχϯάϞσϧΛػث্Ͱ௚઀࣮ߦͰ͖ ΔɺϓϩάϥϛϯάՄೳͳ৽͍͠ϏσΦΧϝϥ wͲΜͳεΩϧϨϕϧͷ։ൃऀͰ΋෼Ͱ%FFQ -FBSOJOHΛ։࢝Ͱ͖Δ SF*OWFOU

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w"NB[PO3FLPHOJUJPO7JEFP wը૾෼ੳαʔϏε"NB[PO3FLPHOJUJPO͕ɺಈը Λαϙʔτ w෺ମɺγʔϯɺςΩετɺإͷݕग़ɺ༗໊ਓͷೝࣝ wܞଳి࿩ɺΧϝϥɺ*P5ϏσΦηϯαʔɺ͓ΑͼϦΞ ϧλΠϜϥΠϒετϦʔϜϏσΦॲཧ͔ΒΩϟϓ νϟʔ͞ΕͨಈըΛɺεέʔϥϒϧͰߴਫ਼౓Ͱಈը ෼ੳ͢ΔιϦϡʔγϣϯʹར༻ SF*OWFOU

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"NB[PO4BHFNBLFS

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wػցֶशϞσϧͷߏஙͱτϨʔχϯάͷ४ උ͕ΑΓ؆୯ʹ wΞϓϦέʔγϣϯʹ࠷దͳΞϧΰϦζϜͱϑϨʔϜ ϫʔΫΛબ୒ w࠷దԽ͢ΔͨΊʹඞཁͳπʔϧ͕ἧ͍ͬͯΔ "NB[PO4BHF.BLFS

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wτϨʔχϯάσʔλΛ؆୯ʹ෼ੳ͠ՄࢹԽ wϗετܕͷ+VQZUFS/PUFCPPLΛඋ͍͑ͯΔ w4ͷσʔλʹ௚઀઀ଓͰ͖Δ w"NB[PO%ZOBNP%#ɺ"NB[PO3FETIJGU͔Β ͷσʔλΛ4ʹҠಈͯͦ͠ΕΒͷσʔλΛ /PUFCPPLͰ෼ੳͰ͖Δ "NB[PO4BHF.BLFS

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Ͳ͏มΘΔͷ͔ʁ

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ैདྷͰ͸ʜ w(16͕ࡌ͍ͬͯΔΠϯελϯεΛ༻ҙ͢Δ

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ैདྷͰ͸ʜ wػցֶशϑϨʔϜϫʔΫΛ౥ࡌ͍ͯ͠Δ".* Λىಈ͢Δ

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ैདྷͰ͸ʜ wֶश༻ͷϓϩάϥϜΛ४උ͢Δ model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=X_train.shape[1:])) model.add(Activation('relu')) model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), padding='same')) model.add(Activation('relu')) model.add(Conv2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

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ैདྷͰ͸ʜ w࣮ߦ

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ैདྷͰ͸ʜ w(16Πϯελϯεͷ༻ҙɺֶश༻ͷϓϩά ϥϜͷ࡞੒ͳͲɺ৭ʑͱϋʔυϧ͕͋ͬͨ w5FOTPS'MPX,FSBTͱ͍ͬͨɺػցֶ श༻ͷϑϨʔϜϫʔΫΛ࢖Θͳ͍ͱݫ͍͠ w্هͷϑϨʔϜϫʔΫͷ஌͕ࣝෆՄܽ wͦ΋ͦ΋ΞϧΰϦζϜͷ஌ࣝ΋ඞཁ

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$*'"3 IUUQXXXDTUPSPOUPFEVdLSJ[DJGBSIUNM

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$*'"3 wYͷը૾ຕͷσʔληοτ wτϨʔχϯάσʔλ͕ຕ wςετσʔλ͕ຕ wτϨʔχϯάσʔλͰֶशͨ͠ͷͪɺςε τσʔλΛ࢖ͬͯݕূ͢Δ

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৞ΈࠐΈχϡʔϥϧωοτϫʔΫ IUUQTXXXZPVUVCFDPNXBUDI UJNF@DPOUJOVFW2;)$1OXX

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৞ΈࠐΈχϡʔϥϧωοτϫʔΫ IUUQTLFSBTJPKB wରԠ͍ͯ͠ΔϥΠϒϥϦͷબఆ

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wϥΠϒϥϦʹैͬͯίʔσΟϯά model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=X_train.shape[1:])) model.add(Activation('relu')) model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), padding='same')) model.add(Activation('relu')) model.add(Conv2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) ৞ΈࠐΈχϡʔϥϧωοτϫʔΫ

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Ͳ͔͜ΒखΛ͚ͭΕ͹͍͍ͷ͔ʜ

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"84SF*OWFOU/&8 -"6/$)*OUSPEVDJOH"NB[PO 4BHF.BLFS .$-

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IUUQTXXXZPVUVCFDPNXBUDI WQC9ETK;Y@L

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε 㲔

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε 㲔

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ֶशϞσϧ࡞੒·Ͱͷγʔέϯε

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OPUFCPPLͷ࡞੒

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OPUFCPPLͷ࡞੒

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OPUFCPPLͷ࡞੒

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OPUFCPPLͷىಈ w*O4FSWJDFʹͳͬͨΒɺ0QFOΛΫϦοΫ͢ Δͱىಈ͢Δ

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OPUFCPPLͷىಈ IUUQTHJUIVCDPNBXTMBCTBNB[POTBHFNBLFSFYBNQMFTUSFFNBTUFS TBHFNBLFSQZUIPOTELNYOFU@HMVPO@DJGBS wαϯϓϧͷʮTBNQMFOPUFCPPLʯ ʮTBHFNBLFSQZSIPOTELʯ ʮNYOFU@DJGBSʯΛࢼ͠ʹ࣮ߦ͢Δ

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δϣϒͷ࡞੒ͷ४උ

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δϣϒͷ࡞੒ͷ४උ wඞཁͳϥΠϒϥϦͷΠϯετʔϧ

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δϣϒͷ࡞੒ͷ४උ

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δϣϒͷ࡞੒ w*O<>ͷۭཝΛΫϦοΫ͢Δ

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δϣϒͷ࡞੒

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δϣϒͷ࡞੒ wδϣϒ͕࡞੒͞ΕΔͱֶश͕࣮ߦ͞ΕΔ

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δϣϒͷ࣮ߦ

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δϣϒͷ࣮ߦ wOPUFCPPL͔ΒֶशͷਐḿΛ֬ೝͰ͖Δ

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ֶशϞσϧͷ࡞੒ wδϣϒ͕੒ޭ͢ΔͱɺֶशϞσϧ͕࡞੒͞ ΕΔ

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ֶशϞσϧͷ࡞੒ wδϣϒ͕੒ޭ͢ΔͱɺֶशϞσϧ͕࡞੒͞ ΕΔ

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ֶशϞσϧͷ࡞੒ wֶशϞσϧͷৄࡉΛ֬ೝ

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ֶशϞσϧͷ࡞੒

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ֶशϞσϧͷ࡞੒ wֶशϞσϧ͸μ΢ϯϩʔυ͢Δ͜ͱ͕Մೳ

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wֶश؀ڥΛ४උ͢Δ·Ͱʹ͔͔͍ͬͯͨί ετΛ௿ݮ wֶशͷਐḿΛՄࢹԽ wαϯϓϧΛར༻͢Δ͜ͱͰʮࣗ਎ͷֶशʯ ͷͱ͔͔ͬΓʹ͢Δ͜ͱ͕Մೳ ·ͱΊ

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ࠓޙ΍Γ͍ͨ͜ͱ IUUQTBXTBNB[PODPNKQCMPHTOFXTBNB[POTBHFNBLFS

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ࠓޙ΍Γ͍ͨ͜ͱ IUUQTBXTBNB[PODPNKQCMPHTOFXTCSJOHNBDIJOFMFBSOJOHUPJPTBQQT VTJOHBQBDIFNYOFUBOEBQQMFDPSFNM